How To Turn Online Data Into a Pricing Strategy That Works

Summary

Ken Moon, a Wharton professor, shares his research on consumer behavior and pricing strategies, highlighting how data-driven insights can optimize decision-making. His work examines how tracking customer behavior across both online and offline interactions reveals patterns of price sensitivity. The research emphasizes that simple pricing strategies—like predictable markdowns with some unpredictability—can increase sales while improving consumer welfare. Moon also advises retailers to segment customers based on their price sensitivity and use basic data signals for targeted promotions, suggesting that even simple pricing policies can be highly effective in driving profitability.

Takeaways

• 😀 Ken Moon's research focuses on empirical operations, using data to improve decision-making in retail environments.
• 📊 The research explores how customer behavior is tracked both online and offline, offering insights into price sensitivity and buying patterns.
• 💡 Price-sensitive customers frequently monitor prices, while less price-sensitive customers check prices much less often, typically every 20 days on average.
• 🔍 Retailers can use simple, predictable pricing strategies with unpredictable markdown timings to maximize revenue by leveraging customer behavior insights.
• 🛒 For price-insensitive customers, making pricing policies predictable and simple helps reduce the opportunity cost of monitoring prices, leading to earlier purchases.
• 📉 Simple pricing models, like initial list prices, predictable sales prices, and clearance prices, can effectively manage customer expectations and maximize sales.
• 💻 Companies can track granular customer data, such as purchase-to-visit ratios, to identify price-sensitive consumers and offer tailored promotions or coupons.
• 🌍 Ken highlights how pricing strategies in industries like airlines, which use dynamic pricing, can be applied in retail to improve product allocation and customer satisfaction.
• ⚖️ A key takeaway is that pricing policies that seem simple and predictable may benefit both consumers and firms, especially when combined with targeted data analysis.
• 🔮 Future research will explore how informational frictions and data use in different sectors (e.g., healthcare, online marketplaces, and workplaces) affect decision-making and consumer comfort.
• 📈 Retailers should focus on understanding why their pricing policies work, using data to refine strategies that avoid misleading customers or creating unrealistic expectations.

Q & A

What is the focus of Ken Moon's research?

Ken Moon's research focuses on empirical operations, particularly using data to improve decision-making in sectors like retail, hospitals, and marketplaces. He aims to provide prescriptive insights based on real-world data.

What kind of data did Ken Moon analyze in his research with a retailer?

Ken analyzed a detailed customer-level dataset that tracked both online and offline consumer behaviors, including activities such as browsing products online, checking prices, and making purchases in physical stores.

What was the main insight from the research about price monitoring by consumers?

The research found that price-sensitive consumers tend to monitor prices frequently, while price-insensitive consumers monitor prices less often. Price-sensitive customers check prices more regularly, which has a significant impact on purchasing behavior.

How does information richness influence consumer behavior, according to the study?

The study suggests that the availability of abundant information, such as price tracking across devices, creates asymmetries between consumers. Consumers with the time and inclination to monitor prices closely are more likely to benefit from discounts, while those who don't monitor as frequently are at a disadvantage.

What was the effect of unpredictable pricing strategies on consumer behavior?

The research found that unpredictable pricing, such as random markdowns, exacerbates the information asymmetry. Price-insensitive consumers, who don't monitor frequently, are less likely to take advantage of discounts when they occur, leading them to purchase earlier at higher prices.

What was the simple pricing policy the retailer used, and why was it effective?

The retailer used a simple pricing policy where the product starts at a list price, drops to a predictable sale price after some time, and eventually reaches a clearance price. This approach was effective because it created a predictable pricing structure while introducing unpredictability in timing, leading to optimal sales.

How do simple pricing policies benefit retailers?

Simple pricing policies, such as predictable markdowns, can be highly effective for retailers. They allow companies to capture most of the value, sell more units, and get products into the hands of the right consumers while avoiding the complexities of more dynamic pricing models.

What does Ken Moon suggest about the relationship between consumer price sensitivity and pricing strategies?

Ken Moon suggests that understanding consumer price sensitivity is crucial for effective pricing. Retailers should identify price-sensitive customers using data, and target them with special offers, discounts, or coupons. He highlights that even simple behavioral data like frequency of visits and purchases can indicate price sensitivity.

What future research does Ken Moon plan to conduct?

Ken plans to explore informational costs and frictions in other industries, including online marketplaces and workplaces. He is interested in understanding how data affects decision-making, both for firms experimenting with data and for consumers and workers navigating data-driven environments.

How can retailers apply Ken Moon's findings in their pricing strategies?

Retailers can apply Ken Moon's findings by using data to understand customer behavior, such as tracking the frequency of visits and purchases to gauge price sensitivity. Additionally, implementing simple yet flexible pricing policies that introduce unpredictability in timing can help retailers capture more value and optimize sales.